However, we can do much better than that: PyTorch integrates with TensorBoard, a tool designed for visualizing the results of neural network training runs. This tutorial illustrates some of its functionality, using the Fashion-MNIST dataset which can be read into PyTorch using torchvision.datasets.

Building models with the neural network layers and functions of the torch.nn module. The mechanics of automated gradient computation, which is central to gradient-based model training. Using TensorBoard to visualize training progress and other activities. In this video, we’ll be adding some new tools to your inventory:

I implemented Transformer from scratch in PyTorch. Why would I do that in the first place? Implementing scientific papers from scratch is something machine learning engineers rarely do these days, at least in my opinion. Most of the machine learning models are already implemented and optimized and all you have to do is tweak some code.

Either downscale the steps at critical points or use this notebook as an inspiration when building a script for distributed training. out = pipe(prompt,...

This repository contains the example code from our O'Reilly book Natural Language Processing with Transformers: You can run these notebooks on cloud platforms like Google Colab or your local machine.

In the first part of this notebook, we will implement the Transformer architecture by hand. As the architecture is so popular, there already exists a Pytorch module nn.Transformer...

In this tutorial, we train nn.TransformerEncoder model on a language modeling task. The language modeling task is to assign a probability for the likelihood of a given word (or a sequence...

A straightforward implementation of the Transformer model in PyTorch, based on the "Attention is All You Need" paper. Includes training, inference, and evaluation notebooks.

This repository demonstrates how to use Matplotlib, one of Python's most popular and versatile plotting libraries, to create detailed visualizations of the Transformer architecture —a revolutionary neural network model introduced in the paper "Attention is All You Need".

A subclass of torch.nn.Module will report the layers it has created and their shapes and parameters. This can provide a handy overview of a model if you want to get the gist of its processing. Below that, we create a dummy input representing a 32x32 image with 1 …